View Proposal

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Proposer

Timothy Yap

Title

Refining Aspect-Based Sentiment Analysis Through Subjectivity in the Pipeline

Goal

To develop and evaluate an enhanced Aspect-Based Sentiment Analysis (ABSA) pipeline that incorporates subjectivity analysis, in order to improve the accuracy and granularity of sentiment interpretation in customer feedback for a specific product or service domain.

Description

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained approach to sentiment analysis that identifies specific aspects of a product, service, or entity within text and determines the sentiment expressed toward each one. Unlike traditional sentiment analysis, which focuses on overall sentiment, ABSA offers more detailed insights. Subjectivity, reflecting the extent to which text conveys personal opinions or emotions rather than objective facts, plays a key role in interpreting sentiment. This project aims to design and evaluate an enhanced ABSA pipeline that integrates subjectivity analysis to better process customer feedback in a specific domain. By incorporating subjectivity into the pipeline, the goal is to improve ABSA accuracy and provide actionable insights into customer sentiment on specific aspects, helping businesses respond more effectively to user concerns.

Resources

Python, Deep Neural Nets

Background

Url

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

1

Supervisor

Timothy Yap

Keywords

sentiment analysis, aspect-based, neural network, machine learning

Degrees

Bachelor of Science in Computing Science